下载K-wave包

文件路径k-wave-toolbox-version-1.4\k-Wave\examples\example_pr_2D_TR_circular_sensor.m

matlab版本R2023b

下载之后 用matlab打开所在文件夹，然后将整个文件夹及其子文件夹添加到路径

因为版本原因 Imageload已经不再用了 所以改成imread

p0 = p0_magnitude * imread('EXAMPLE_source_two.bmp');

根据example_pr_2D_TR_circular_sensor.m文件改成声源是一个圆形的肿瘤，传感器只有三个，没有噪声的正演图，并绘制出三个传感器得到的data。

1. 肿瘤位置大小设置

% 定义肿瘤的半径和中心位置
tumor_radius = 1e-3; % 肿瘤半径 [m] 
tumor_center_x = (Nx / 2); % 肿瘤中心 x 坐标
tumor_center_y = (Ny / 2); % 肿瘤中心 y 坐标

% 将圆形肿瘤的形状添加到初始压力分布中
for i = 1:Nx
    for j = 1:Ny
        % 计算当前点到肿瘤中心的距离
        distance = sqrt((i - tumor_center_x)^2 + (j - tumor_center_y)^2);
        % 如果距离小于或等于肿瘤半径，则将该点的压力设置为 1
        if distance <= tumor_radius
            p0(i, j) = 1;
        end
    end
end

2. 传感器个数

num_sensor_points = 3;

3. 删除反演的过程

4. 加入绘制sensor_data的图（注意 这里的sensor_data是一个矩阵，得转置一下才能让每行的数据生成一条曲线）

% plot the simulated sensor data
figure;
plot(sensor_data')

给出完整代码

% assign the grid size and create the computational grid
PML_size = 20;              % size of the PML in grid points
Nx = 256 - 2 * PML_size;    % number of grid points in the x direction
Ny = 256 - 2 * PML_size;    % number of grid points in the y direction
x = 10e-3;                  % total grid size [m]
y = 10e-3;                  % total grid size [m]
dx = x / Nx;                % grid point spacing in the x direction [m]
dy = y / Ny;                % grid point spacing in the y direction [m]
kgrid = kWaveGrid(Nx, dx, Ny, dy);


% 定义肿瘤的半径和中心位置
tumor_radius = 1e-3; % 肿瘤半径 [m] 
tumor_center_x = (Nx / 2); % 肿瘤中心 x 坐标
tumor_center_y = (Ny / 2); % 肿瘤中心 y 坐标

% 创建一个与网格大小相同的零矩阵作为初始压力分布
p0 = zeros(Nx, Ny);

% 将圆形肿瘤的形状添加到初始压力分布中
for i = 1:Nx
    for j = 1:Ny
        % 计算当前点到肿瘤中心的距离
        distance = sqrt((i - tumor_center_x)^2 + (j - tumor_center_y)^2);
        % 如果距离小于或等于肿瘤半径，则将该点的压力设置为 1
        if distance <= tumor_radius
            p0(i, j) = 1;
        end
    end
end

% resize the input image to the desired number of grid points
p0 = resize(p0, [Nx, Ny]);

% smooth the initial pressure distribution and restore the magnitude
p0 = smooth(p0, true);

% assign to the source structure
source.p0 = p0;

% define the properties of the propagation medium
medium.sound_speed = 1500;  % [m/s]

% define a centered Cartesian circular sensor
sensor_radius = 4.5e-3;     % [m]
sensor_angle = 3*pi/2;      % [rad]
sensor_pos = [0, 0];        % [m]
num_sensor_points = 3;
cart_sensor_mask = makeCartCircle(sensor_radius, num_sensor_points, sensor_pos, sensor_angle);

% assign to sensor structure
sensor.mask = cart_sensor_mask;

% create the time array
kgrid.makeTime(medium.sound_speed);

% set the input options
input_args = {'Smooth', false, 'PMLInside', false, 'PlotPML', false};

% run the simulation
sensor_data = kspaceFirstOrder2D(kgrid, medium, source, sensor, input_args{:});

% plot the initial pressure and sensor distribution
figure;
imagesc(kgrid.y_vec * 1e3, kgrid.x_vec * 1e3, p0 + cart2grid(kgrid, cart_sensor_mask), [-1, 1]);
colormap(getColorMap);
ylabel('x-position [mm]');
xlabel('y-position [mm]');
axis image;

% plot the simulated sensor data
figure;
plot(sensor_data')

运行结果